Modeling potentially hazardous sites and predicting hazardous conditions

ABSTRACT

Implementations include methods for monitoring and reporting on actual hazardous conditions at a facility including actions of receiving data associated with a site, the site being susceptible to potentially hazardous conditions, processing the data, one or more models, and one or more prediction rules, determining that a hazardous condition is predicted to occur at the site, providing output data reflecting the hazardous condition, processing the output data to provide indicator data for providing a graphical representation of the site, the graphical representation providing a graphical depiction of the hazardous condition, and providing the indicator data to one or more user devices, the indicator data being processed by each of the one or more user devices to display the graphical representation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/546,273, filed Nov. 18, 2014, which claims priority to U.S. Prov.App. No. 61/908,452, filed Nov. 25, 2013, which are expresslyincorporated herein by reference in their entirety.

BACKGROUND

Sites, such as oil and gas well-sites, can incur hazardous conditions.Example hazardous conditions can include the presence of gas that canhave adverse effects, if inhaled. In some cases, personnel visit sitesto remedy hazardous conditions, which can cost time and money, as wellas expose personnel to potential risk. Avoiding occurrences of hazardousconditions can reduce time and cost associated with management of asite, and can mitigate risk.

SUMMARY

Implementations of the present disclosure include computer-implementedmethods for modeling potentially hazardous sites and predictinghazardous conditions. In some implementations, actions include receivingdata associated with a site, the site being susceptible to potentiallyhazardous conditions, processing the data, one or more models, and oneor more prediction rules, determining that a hazardous condition ispredicted to occur at the site, providing output data reflecting thehazardous condition, processing the output data to provide indicatordata for providing a graphical representation of the site, the graphicalrepresentation providing a graphical depiction of the hazardouscondition, and providing the indicator data to one or more user devices,the indicator data being processed by each of the one or more userdevices to display the graphical representation. Other implementationsinclude corresponding systems, apparatus, and computer programs,configured to perform the actions of the methods, encoded on computerstorage devices.

These and other implementations can each optionally include one or moreof the following features: the output data includes one or morepredicted values reflective of the hazardous condition, a predictedvalue including a value that is provided based on an actual value andthe one or more models, the one or more models include at least one of asite model, a fluid-flow model, and a weather model, the site modelmodels physical features of the site, the site model modelstopographical features of the site, topographical features of the siteinclude topographical features that are within a threshold distance fromthe site, the fluid-flow model models flow of one or more fluids, theone or more fluids include at least one of hydrogen sulfide (H2S),methane (CH4), carbon monoxide (CO), and carbon dioxide (CO2), dataincludes data measured at the site, the data includes weather datareceived from one or more weather sources, the weather data includes atleast one of local weather data, regional weather data and nationalweather data, a weather source includes a weather station located at thesite, the graphical representation includes an indicator of thehazardous condition at the site, the indicator includes location andseverity of the hazardous condition with respect to the site, thegraphical representation further includes a time indicator, the timeindicator indicating a date and/or time, at which the hazardouscondition is predicted to be present, the site includes at least one ofa production well-site, an exploration well-site, a configurationwell-site, an injection well-site, an observation well-site, and adrilling well-site, the site includes at least a portion of anabove-ground appurtenance, the above-ground appurtenance includes apipeline, the site includes an intermediate site located betweenend-point sites, and an end-point site includes one of a well-site and arefinery.

The present disclosure also provides a computer-readable storage mediumcoupled to one or more processors and having instructions stored thereonwhich, when executed by the one or more processors, cause the one ormore processors to perform operations in accordance with implementationsof the methods provided herein.

The present disclosure further provides a system for implementing themethods provided herein. The system includes one or more processors, anda computer-readable storage medium coupled to the one or more processorshaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform operationsin accordance with implementations of the methods provided herein.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also include any combination of the aspects andfeatures provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 depicts an example system in accordance with implementations ofthe present disclosure.

FIG. 2 depicts an example portion of a play network.

FIG. 3 depicts a representation of an example well-site.

FIG. 4 depicts an example screen-shot in accordance with implementationsof the present disclosure.

FIGS. 5A-5C depict example screen-shots in accordance withimplementations of the present disclosure.

FIG. 6 depicts an example processes that can be executed in accordancewith implementations of the present disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Implementations of the present disclosure are generally directed tomonitoring potentially hazardous sites and predicting hazardousconditions. More specifically, implementations of the present disclosureprocess data associated with potentially hazardous sites based on one ormore models. In some examples, the data includes data associated withequipment located at the site. In some examples, the data includessensor data from one or more sensors located at the site. In someexamples, the data includes topographical data associated with the site.In some examples, the data includes weather data, e.g., local, regional,national, corresponding to weather that can affect and/or does affectthe site. In accordance with implementations of the present disclosure,the one or more models and the data are processed to predict occurrenceof hazardous conditions occurring at the site. Further, the data and theone or more models are processed to determine a potential extent, e.g.,time, location and/or severity of the predicted hazardous conditions. Insome implementations, one or more graphical user interfaces (GUIs) canbe presented on computing devices, which depict representations of thepredicted hazardous conditions at the site.

Implementations of the present disclosure are generally applicable tosites that have potential to have hazardous conditions present. In someexamples, hazardous conditions can include conditions that could bephysically harmful to humans, animals, and/or vegetation. Exampleconditions can include the presence of a hazardous fluid, e.g., gas,liquid.

Implementations of the present disclosure will be discussed in furtherdetail with reference to an example context. The example contextincludes oil and gas well-sites. It is appreciated, however, thatimplementations of the present disclosure can be realized in otherappropriate contexts, e.g., a chemical plant, a fertilizer plant, tankbatteries (located away from a site), above-ground appurtenances(pipelines) and/or intermediate sites. An example intermediate site caninclude a central delivery point that can be located between a site anda refinery, for example. Within the example context, implementations ofthe present disclosure are discussed in further detail with reference toan example sub-context. The example sub-context includes a productionwell-site. It is appreciated, however, that implementations of thepresent disclosure can be realized in other appropriate sub-contexts,e.g., an exploration well-site, a configuration well-site, an injectionwell-site, an observation well-site, and a drilling well-site.

In the example context and sub-context, well-sites can be located innatural resource plays. A natural resource play can be associated withoil and/or natural gas. In general, a natural resource play includes anextent of a petroleum-bearing formation, and/or activities associatedwith petroleum development in a region. An example geographical regioncan include southwestern Texas in the United States, and an examplenatural resource play includes the Eagle Ford Shale Play.

FIG. 1 depicts an example system 100 that can execute implementations ofthe present disclosure. The example system 100 includes one or morecomputing devices, such as computing devices 102, 104, one or more playnetworks 106, and a computing cloud 107 that includes one or morecomputing systems 108. The example system 100 further includes a network110. The network 110 can include a large computer network, such as alocal area network (LAN), wide area network (WAN), the Internet, acellular network, a satellite network, a mesh network, e.g., 900 Mhz,one or more wireless access points, or a combination thereof connectingany number of mobile clients, fixed clients, and servers. In someexamples, the network 110 can be referred to as an upper-level network.

The computing devices 102, 104 are associated with respective users 112,114. In some examples, the computing devices 102, 104 can each includevarious forms of a processing device including, but not limited to, adesktop computer, a laptop computer, a tablet computer, a wearablecomputer, a handheld computer, a personal digital assistant (PDA), acellular telephone, a network appliance, a smart phone, an enhancedgeneral packet radio service (EGPRS) mobile phone, or an appropriatecombination of any two or more of these example data processing devicesor other data processing devices. The computing systems 108 can eachinclude a computing device 108 a and computer-readable memory providedas a persistent storage device 108 b, and can represent various forms ofserver systems including, but not limited to a web server, anapplication server, a proxy server, a network server, or a server farm.

In some implementations, and as discussed in further detail herein, sitedata (e.g., oil data and/or gas data) can be communicated from one ormore of the play networks 106 to the computing systems 108 over thenetwork 110. In some examples, each play network 106 can be provided asa regional network. For example, a play network can be associated withone or more plays within a geographical region. In some examples, eachplay network 106 includes one or more sub-networks. As discussed infurther detail herein, example sub-networks can include a low power datasub-network, e.g., a low power machine-to-machine data network (alsoreferred to as a smart data network and/or an intelligent data network,one or more wireless sub-networks, and mesh sub-networks, e.g., 900 Mhz.

In some examples, the computing systems 108 store the well data and/orprocess the well data to provide auxiliary data. In some examples, thewell data and/or the auxiliary data are communicated over the playnetwork(s) 106 and the network 110 to the computing devices 102, 104 fordisplay thereon. In some examples, user input to the computing devices102, 104 can be communicated to the computing systems 108 over thenetwork 110.

In general, monitoring of well sites can include oil well monitoring andnatural gas well monitoring (e.g., pressure(s), temperature(s), flowrate(s)), compressor monitoring (e.g., pressure, temperature), flowmeasurement (e.g., flow rate), custody transfer, tank level monitoring,hazardous gas detection, remote shut-in, water monitoring, cathodicprotection sensing, asset tracking, water monitoring, access monitoring,and valve monitoring. In some examples, monitoring can includemonitoring the presence and concentration of fluids (e.g., gases,liquids). In some examples, control capabilities can be provided, suchas remote valve control, remote start/stop capabilities, remote accesscontrol.

FIG. 2 depicts an example portion of an example play network 200. Theexample play network 200 provides low power (LP) communication, e.g.,using a low power data network, and cellular and/or satellitecommunication for well data access and/or control. In some examples, asdiscussed herein, LP communication can be provided by a LP network. Inthe example of FIG. 2, a first well site 202, a second well site 204 anda third well site 206 are depicted. Although three well sites aredepicted, it is appreciated that the example play network 200 caninclude any appropriate number of well sites. In the example of FIG. 2,well monitoring and data access for the well site 202 is provided usingLP communication and cellular and/or satellite communication, and wellmonitoring and data access for the well sites 204, 206 is provided usingcellular, satellite, and/or mesh network communication.

The example of FIG. 2 corresponds to the example context and sub-context(a production well-site) discussed above. It is appreciated, however,that implementations of the present disclosure. In the depicted example,the well site 202 includes a wellhead 203, a sensor system 210, a sensorsystem 212 and communication device 214. In some examples, the sensorsystem 210 includes a wireless communication device that is connected toone or more sensors, the one or more sensors monitoring parametersassociated with operation of the wellhead 203. In some examples, thewireless communication device enables monitoring of discrete and analogsignals directly from the connected sensors and/or other signalingdevices. In some examples, the sensor system 210 can provide controlfunctionality (e.g., valve control). Although a single sensor system 210is depicted, it is contemplated that a well site can include anyappropriate number of sensor systems 210. In some examples, the sensorsystem 212 includes one or more sensors that monitor parametersassociated with operation of the wellhead 203. In some examples, thesensor system 212 generates data signals that are provided to thecommunication device 214, which can forward the data signals. Although asingle sensor system 212 and communication device 214 are depicted, itis contemplated that a well site can include any appropriate number ofsensor systems 212 and/or communication devices 214.

Well data and/or control commands can be provided to/from the well site202 through an access point 216. More particularly, information can betransmitted between the access point 216, the sensor system 210, and/orthe communication device 214 based on LP. In some examples, LP providescommunication using a globally certified, license free spectrum (e.g.,2.4 GHz). In some examples, the access point 216 provides a radialcoverage that enables the access point 216 to communicate with numerouswell sites, such as the well site 202. In some examples, the accesspoint 216 further communicates with the network 110 using cellular,satellite, mesh, point-to-point, point-to-multipoint radios, and/orterrestrial or wired communication.

In the depicted example, the access point 216 is mounted on a tower 220.In some examples, the tower 220 can include an existingtelecommunications or other tower. In some examples, an existing towercan support multiple functionalities. In this manner, erection of atower specific to one or more well sites is not required. In someexamples, one or more dedicated towers could be erected.

In the depicted example, the well sites 204, 206 include respectivewellheads 205, 207, and respective sensor systems 210 (discussed above).Although a single sensor system 210 is depicted for each well site 204,206, it is contemplated that a well site can include any appropriatenumber of sensor systems 210. In some examples, well data and/or controlcommands can be provided to/from the well sites 202 through a gateway232. More particularly, information can be transmitted between thegateway 232, and the sensor systems 210 can be wireless communication(e.g., radio frequency (RF)). In some examples, the gateway 232 furthercommunicates with the network 110 using cellular and/or satellitecommunication.

In accordance with implementations of the present disclosure, well sitecontrol and/or data visualization and/or analysis functionality (e.g.,hosted in the computing cloud 107 of FIGS. 1 and 2) and one or more playnetworks (e.g., the play networks 106, 200 of FIGS. 1 and 2) can beprovided by a service provider. In some examples, the service providerprovides end-to-end services for a plurality of well sites. In someexamples, the service provider owns the one or more play networks andenables well site operators to use the play networks andcontrol/visualization/monitoring functionality provided by the serviceprovider. For example, a well site operator can operate a plurality ofwell sites. The well site operator can engage the service provider forwell site control/visualization/monitoring services (e.g., subscribe forservices). In some examples, the service provider and/or the well siteoperator can install appropriate sensor systems, communication devicesand/or gateways (e.g., as discussed above with reference to FIG. 2). Insome examples, sensor systems, communication devices and/or gateways canbe provided as end-points that are unique to the well site operator.

In some implementations, the service provider can maintain one or moreindices of end-points and well site operators. In some examples, theindex can map data received from one or more end-points to computingdevices associated with one or more well site operators. In someexamples, well site operators can include internal server systems and/orcomputing devices that can receive well data and/or auxiliary data fromthe service provider. In some examples, the service provider can receivemessages from well sites, the messages can include, for example, welldata and an end-point identifier. In some examples, the service providercan route messages and/or auxiliary data generated by the serverprovider (e.g., analytical data) to the appropriate well site operatoror personnel based on the end-point identifier and the index. Similarly,the service provider can route messages (e.g., control messages) from awell site operator to one or more appropriate well sites.

As introduced above, implementations of the present disclosure aredirected to monitoring potentially hazardous sites and predictingoccurrence of hazardous conditions. More specifically, implementationsof the present disclosure process data associated with potentiallyhazardous sites based on one or more models, and/or one or moreprediction rules. In the example context and sub-context, the siteincludes a production well-site. As discussed in further detail herein,the data can include data associated with equipment located at the site,the data can include sensor data from one or more sensors located at thesite, the data can include topographical data associated with the site,and/or the data can include weather data corresponding to weather thatcan affect or does affect the site. In some examples, data can includeproperties of one or more substances, e.g., fluids, that are monitored.Example properties can include molecular weight, critical point and/orphase properties, e.g., solid, liquid, gaseous.

In some implementations, a model can include a physical model of awell-site. For example, the model can model the type, size and locationof equipment present at the well-site. In some examples, the model caninclude topographical features present at the well-site. Exampletopographical features can include vegetation, dips, valleys, berms,hills, troughs, mountains and the like. In some examples, thetopographical features include features within a threshold distance froma well-site, e.g., within a 5 mile radius of the well-site. In someimplementations, a model can include a weather pattern model of thewell-site. For example, the model can model temperatures, winds andother appropriate meteorological characteristics that can affect thewell-site. In some examples, the weather model can be based on local,regional and/or national weather patterns. In some examples, the weathermodel can process local, regional and/or national weather data. In someimplementations, a model can include a fluid flow model that can modelthe flow of one or more types of fluids at the well-site. In someexamples, the weather data can include current data, e.g., measuredtemperature, barometric pressure, wind-speed, humidity, precipitation.In some examples, the weather data can include forecasted data, e.g.,forecasted temperature, barometric pressure, wind-speed, humidity,precipitation.

In some implementations, a prediction rule can define parameters thatare associated with occurrence of a hazardous condition. In someexamples, a prediction rule can be specific to a particular entitypresent at a well-site. Example entities can include equipment, conduits(piping) and the like. In some examples, a set of prediction rules canbe provided for a particular well-site, the set of prediction rulescomprising prediction rules associated with entities present at theparticular well-site.

In some examples, a prediction rule can associate a hazardous conditionto one or more parameters. By way of example, an example hazardouscondition can include venting of a gas, e.g., H2S, from a storage tank.In this example, a prediction rule can be associated the hazardouscondition to a pressure threshold and/or a temperature threshold. Forexample, the prediction rule can provide that, if a pressure and/or atemperature associated with the storage tank respectively exceed thepressure threshold and/or the temperature threshold, hazardous gas willbe vented from the storage tank, e.g., with some probability. In someexamples, more complex prediction rules can be provided. For example, aprediction rule can provide predicted rates of venting of gases based onpredicted temperatures and/or pressures.

In accordance with implementations of the present disclosure, the one ormore models, the data, and the one or more prediction rules areprocessed to predict occurrences of hazardous conditions at the site.Further, the data, the one or more models, and the one or moreprediction rules are processed to determine an extent, e.g., locationand/or severity of predicted hazardous conditions. In someimplementations, one or more graphical user interfaces (GUIs) can bepresented one computing devices, which depict representations of thepredicted hazardous conditions at the site.

FIG. 3 depicts a representation of an example well-site 300. The examplewell-site 300 can include a production well-site, in accordance with theexample sub-context provided above. In the depicted example, thewell-site 300 includes a well-head 302, an oil and gas separator 304 anda storage tank system 306. In the depicted example, the storage tanksystem 306 includes a manifold 308 and a plurality of storage tanks 310.The example well-site 300 further includes a base station 312. In someexamples, the well-site 300 can include a local weather station 314. Insome examples, the well-site 300 can include artificial lift equipment316, e.g., to assist in extraction of oil and/or gas from the well.

In some examples, the well-site 300 includes one or more sensors 320a-320 g. In some examples, each sensor 320 a-320 g can be provided as asingle sensor. In some examples, each sensor 320 a-320 g can be providedas a cluster of sensors, e.g., a plurality of sensors. Example sensorscan include fluid sensors, e.g., gas sensors, temperature sensors,and/or pressure sensors. Each sensor 320 a-320 g is responsive to acondition, and can generate a respective signal based thereon. In someexamples, the signals can be communicated through a network, asdiscussed above with reference to FIG. 2.

Implementations of the present disclosure will be described in furtherdetail with reference to an example hazardous condition. The examplehazardous condition includes the presence of a hazardous gas. It isappreciated that implementations of the present disclosure areapplicable to other appropriate hazardous conditions. Example hazardousgases can include hydrogen sulfide (H2S), methane, carbon monoxide (CO),carbon dioxide (CO2). Implementations of the present disclosure will bedescribed in further detail with reference to H2S. In some examples, ahazardous gas might not be hazardous to humans, for example, insufficiently small concentrations, e.g., less than a threshold parts permillion (PPM). In some examples, a hazardous gas can be hazardous insufficiently high concentrations, e.g., equal to or greater than thethreshold PPM.

Referring again to FIG. 3, sensors 320 a-320 g can include temperaturesensors and/or pressure sensors. For example, the sensors 320 a-320 gcan be responsive to the temperature and/or pressure of a fluid. Thatis, the sensors 320 a-320 g can generate respective signals thatindicate the temperature and/or pressure of a fluid.

As discussed herein, data from the sensors 320 a-320 g can be providedto a back-end system for processing. For example, data can be providedthrough a play network, e.g., the play network(s) 106 of FIG. 1, to acomputing cloud, e.g., the computing cloud 107. The computing cloud canprocess the data and other data, as well as one or more models and oneor more prediction rules, to provide output to one or more computingdevices, e.g., the computing devices 102, 104 of FIG. 1. For example,and as discussed in further detail herein, the computing cloud canprocess the data and the one or more models, and the one or moreprediction rules to predict the occurrence of a hazardous condition,e.g., the presence and concentration of a hazardous gas, and to provideone or more graphical representations of a well-site for display on acomputing device.

In some implementations, the computing cloud can include one or moremodels for each well-site of a plurality of monitored well-sites. Forexample, the one or more models can be stored in computer-readablememory. In some examples, the computing cloud can include propertiesassociated with hazardous materials that can be present at thewell-site. For example, the properties can be stored incomputer-readable memory. Data associated with the well-site can bereceived by the computing cloud. For example, data, e.g., signals,generated at the well-site can be provided to the computing cloudthrough one or more networks. In some examples, one or more externalsources can provide data associated with the well-site. For example,meteorological data can be provided from one or more weather services,e.g., local, regional and/or national weather services. In someexamples, meteorological data can be provided directly from thewell-site, e.g., from a weather station located at the well-site(monitoring wind speed/direction, temperature, humidity, and/orbarometric pressure).

In some implementations, the computing cloud can retrieve the one ormore prediction rules, e.g., from a rule repository. In some examples,the one or more prediction rules can be generic to all sites. In someexamples, the one or more prediction rules can be specific to aparticular site, or a particular set of sites. In some examples, thecloud computing device can retrieve site-specific prediction rules basedon an identifier associated with a particular site.

In some examples, the computing cloud processes the one or more models,the data, and the one or more prediction rules using an engine toprovide output data. In some examples, the output data indicates time,locations and/or concentrations of hazardous gas predicted to be presentat the well-site. In some examples, the computing cloud processes theone or more models, the data and the one or more prediction rules inresponse to a trigger signal. In some examples, the trigger signal canbe provided as a periodic signal, such that occurrence of a hazardouscondition can be periodically determined, e.g., every hour, once a day,once a week. In some examples, the trigger signal can be provided as oneor more sensor signals. For example, if a temperature sensor indicates atemperature that exceeds a threshold temperature, it can be determinedthat the one or more models, the data, and the one or more predictionrules are to be processed predict whether a hazardous condition willoccur. In some examples, the trigger signal can be provided in responseto user input, the user input indicating a request to perform predictionfor one or more well-sites.

In some examples, the output data can be processed to generate graphicalrepresentations, discussed in further detail herein. For example, theoutput data can include an array of gas type, time, location andconcentration data, such that particular locations within the well-siteare associated with a predicted gas concentration at a particular time.In some examples, output data can be provided as a tuple of values. Thefollowing example tuple can be provided:

Output Data=[G, L, C, t]

where G indicates a predicted gas type, e.g., H2S, CO, CO2, CH4, Lindicates a predicted location within a well-site, C indicates apredicted concentration, and t indicates a predicted time. In thisexample, the tuple can indicate a prediction that a gas G having aconcentration C will be present at a location L at time t. In someexamples, a location within a site can include coordinate data, e.g.,x-y coordinates in two-dimensional space, x-y-z coordinates inthree-dimensional space. In some examples, a location can includedifferent concentrations of gas at different times. In some examples, alocation can include multiple gases at a single time.

In some examples, a first value for gas concentration can be provided asa predicted value at a first location, e.g., a pressure relief valve. Asecond value for gas concentration can be provided as a predicted valueat a second location, e.g., a location immediately adjacent to the firstlocation. In some examples, the second value can be provided based onthe first value, one or more previously predicted values associated withthe second location, one or more previously predicted values associatedwith the first location, gas properties, weather data, one or moremodels, e.g., fluid flow model, weather model, model of the well-site,and one or more prediction rules.

In some examples, the output data can also include probability data. Thefollowing example tuple can be provided:

Output Data=[P, G, L, C, t]

where P indicates a probability. In some examples, the probability canbe provided within a range, e.g., from 0 to 1, from 0%-100%. In thisexample, the tuple can indicate a probability (likelihood) that theprediction of a gas G having a concentration C will be at a location Lat time t will come to fruition.

By way of example, and as noted above, an example hazardous conditioncan include venting of a gas, e.g., H2S, from a storage tank. In someexamples, current conditions associated with the storage tank and thewell-site can be determined. Example current conditions can include anactual pressure and/or temperature associated with the storage tank,e.g., based on signals received from pressure and/or temperaturesensors, as well as an ambient temperature associated with thewell-site, e.g., based on signals received from temperatures sensors,and/or meteorological data provided from a weather service. In someexamples, forecasted temperature data can be provided and can indicatean increase in ambient temperature from the current ambient temperature.In accordance with implementations of the present disclosure, it can bedetermined that the pressure and/or temperature associated with thestorage tank will likely exceed respective threshold values based on thepredicted ambient temperature, leading to a hazardous condition.

In accordance with implementations of the present disclosure, the outputdata is processed to provide graphical representations of the predictedhazardous condition at the well-site. In some examples, the graphicalrepresentations include one or more indicators, such as gas maps,discussed in further detail herein, that indicate the predicted presenceand/or concentration of hazardous materials. For example, for eachoutput data tuple, discussed above, an indicator can be generated, andcan be included in the graphical representations. In some examples, fora plurality of output data tuples, a plurality of indicators areprovided, that collectively provide an overall condition indicator. Forexample, each indicator can provide a portion of a condition indicator,e.g., gas map.

In some examples, a characteristic of the indicator can be providedbased on values provided in the output data. Example characteristics caninclude color, shape and/or pattern. In the example case of color, afirst concentration value (or range of values) can be associated with afirst color, and a second concentration value (or range of values) canbe associated with a second color. If a first concentration valueprovided in a first output data tuple corresponds to the firstconcentration value, a first indicator that is provided for the firstoutput data tuple is assigned the first color. Similarly, if a secondconcentration value provided in a second output data tuple correspondsto the second concentration value, a second indicator that is providedfor the second output data tuple is assigned the second color. The firstindicator and the second indicator together can define at least aportion of the condition indicator.

In some examples, the output data is processed to provide an array ofindicator data. In some examples, indicator data can be provided as atuple of values. The following example tuple can be provided:

Indicator Data=[L, X]

where X indicates the characteristic to be displayed at location L. Insome examples, the indicator data is processed to depict the conditionindicator as part of the graphical representation of the well-site.

FIG. 4 depicts an example screen-shot in accordance with implementationsof the present disclosure. The example screen-shot includes a GUI 400that includes a map frame 402 and a sensor type frame 404. In thedepicted example, the map frame 402 depicts a map, e.g., a graphicalrepresentation, of a geographical region, which includes one or morewell-sites. In the depicted example, well-sites can be indicated bymarkers 408. In some examples, the GUI can provide zooming and/orscrolling of the map displayed within the map frame 402 based on userinput.

In some examples, the sensor type frame 404 provides an interface for auser to select a type of sensor, for which data is requested, and/or toprovide filter parameters to affect the map displayed in the map frame402. In the depicted example, safety sensors have been selected andfilter options are provided for H2S, CO2 and lower explosive limit(LEL). For example, the user can provide input to select respectiveconcentration levels to filter well-sites that are depicted in the mapframe 402. That is, the markers 408 can correspond to well-site thatmeet the filter parameters provided in the sensor type frame 404. In thedepicted example, the markers 408 indicates well-sites that include thepredicted presence of H2S in concentrations within the range of 50 PPMto 100 PPM, that include any predicted presence of CO2, and that includepredicted LEL within the range of 30 PPM to 60 PPM.

In some implementations, markers 408 can include graphical indicators410, e.g., halos. In some examples, the indicators 410 can indicate theimminent occurrence of a hazardous condition, e.g., the probability ofthe hazardous condition exceeds a threshold probability. In someexamples, the indicators 410 can be provided independently of filtersettings provided in the sensor type frame 404. For example, it can bedetermined that a particular well-site includes the imminent occurrenceof a hazardous condition. Consequently, a marker 408 and/or indicator410 for the well-site can be provided in the map frame 402, regardlessof whether the filter settings would otherwise filter the well-site frombeing indicated in the map frame 402.

In accordance with implementations of the present disclosure, graphicalrepresentations of well-sites can be provided, which graphically depictthe presence and extent of a predicted hazardous condition. For example,the user can select a marker 408 that includes an indicator 410 and, inresponse to the user selection, a graphical depiction of the well-sitecan be displayed.

FIGS. 5A-5C depict example screen-shots in accordance withimplementations of the present disclosure. More specifically, theexample screen-shots of FIGS. 5A-5C provide GUIs depicting graphicalrepresentations of a well-site. With particular reference to FIG. 5A, aGUI 500 includes a well-site indicator frame 502, a sensor selectionframe 504, and a graphical representation frame 504. In some examples,the well-site indicator frame 502 provides an identifier indicating theparticular well-site being viewed within the GUI 500. In some examples,the sensor selection frame 504 provides a list of sensors present at theparticular well-site based on sensor type. In the depicted example, thesensor type is provided as H2S sensors and, for the particularwell-site, perimeter H2S sensors are provided, e.g., S1, S2, S3, S4, andequipment-specific sensors, e.g., storage tanks, base station, wellheadand compressor. In some examples, the graphical representation frame 504depicts a graphical representation 510 of the well-site identified inthe well-site indicator frame 502. In some examples, the graphicalrepresentation 510 includes an image of the actual well-site, e.g., asatellite image, an aerial image. In some examples, the graphicalrepresentation 510 includes a representation based on the actualwell-site, e.g., a drawing of the well-site. In the depicted example,the well-site of the graphical representation 510 includes the examplewell-site 300 of FIG. 3.

FIG. 5B depicts the graphical representation 510 corresponding to apredicted hazardous condition that has potential to be present at thewell-site. In this example, the hazardous condition includes thepresence of H2S, e.g., venting of H2S from one or more storage tanks. Insome implementations, a condition indicator 520 can be provided. In someexamples, the condition indicator 520 is provided based on processing ofthe data, the one or more models and the one or more prediction rules,as discussed above. In some examples, the condition indicator isprovided as a gas map 520, a graphical representation of an actualand/or estimated presence of H2S at the well-site. In the depictedexample, the gas map 520 is provided as a heat map that includes aplurality of indicators 522, 524, 526. In some examples, each indicator522, 524, 526 indicates a predicted concentration, e.g., in PPM, of H2S.In some examples, each indicator 522, 524, 526 can be provided as arespective color and/or pattern that is distinct from colors and/orpatterns of other indicators 522, 524, 526.

In some examples, the gas map 520 can be indicative of a first time, orfirst period of time. For example, the gas map 520 can correspond to atime period, during which H2S is vented from storage tanks. In someexamples, the gas map 520 can be animated to depict a progression of thepresence of H2S at the well-site during the period of time. In someexamples, a time indicator 530 can be provided. In some examples, thetime indicator 530 indicates the data and/or time (or time range) atwhich the predicted condition, graphically represented by the conditionindicator 520, is predicted to occur.

FIG. 5C depicts the graphical representation 510 including a gas map520′ at a second time, or second period of time. In the example of FIG.5C, the gas map 520′ includes indicators 528, 530. In some examples, thegas map 520′ can correspond to time period, during which H2S ispredicted to cease being vented from storage tanks, and is dispersingfrom the well-site. For example, the indicator 528 can represent H2Sthat is predicted to pool between storage tanks, and the indicator 530can represent H2S that is predicted to disperse from the well-site.

In accordance with implementations of the present disclosure, thegraphical representations of the predicted hazardous condition enablesusers to remotely evaluate the well-site. In some examples, visits to awell-site can be scheduled based on predicted hazardous conditions. Forexample, if a hazardous condition is predicted, e.g., within a thresholdprobability, one or more technicians can be dispatched to the well-siteto institute remedial measures in an effort to avoid occurrence of thepredicted hazardous condition.

Implementations of the present disclosure also enable evaluation ofwell-site design. For example, predicted hazardous conditions canindicate a flaw in the design of a well-site, and can server as a basisfor correcting the design at the well-site, and/or at other, similarlypositioned/structured well-sites. By way of example, a predictedhazardous condition can be associated with above-ground piping of awell-site. In some examples, the initial design of the well-site couldhave indicated that predicted well-site conditions were such thatabove-ground piping could be used, e.g., which can be a cost-savings ascompared to under-ground piping. In some examples, predicted hazardousconditions associated with the above-ground piping can indicate a flawin the design, e.g., in view of the expected conditions. For example,actual well-site conditions can be different than the predictedwell-site conditions, in view of which the well-site was designed.Accordingly, it can be determined that the above-ground piping should bechanged to under-ground piping to avoid predicted hazardous conditions.

FIG. 6 depicts an example process 600 that can be executed in accordancewith implementations of the present disclosure. In some examples, theexample process 600 can be provided as one or more computer-executableprograms executed using one or more computing devices. In some examples,the example process 600 can be executed for a particular facility, e.g.,well-site.

Field data is received (602). For example, a computing cloud, e.g., thecomputing cloud 107 of FIG. 1, can receive field data. In some examples,the field data can be provided based on signals of sensors provided fromone or more well-sites. One or more models and one or more predictionrules are received (604). For example, the computing cloud can receivethe model(s) and the rule(s). In some examples, models and/or rules canbe specific to the particular site. The model(s), rule(s) and field dataare processed (606). For example, the field data is processed by thecomputing cloud to predict the presence and/or concentration of ahazardous material, e.g., gas.

It is determined whether a hazardous condition is predicted (608). Forexample, values of the field data can be processed based on the models)and rule(s) to predict future values, which future (predicted) valuescan be compared to one or more thresholds values. In some examples, if apredicted value exceeds a threshold value, it can be determined that ahazardous condition is predicted. If it is determined that a hazardouscondition is not predicted, the example process 600 loops back. If it isdetermined that a hazardous condition is predicted, indicator data isprovided (610). In some examples, the computing cloud processes fielddata, data and the one or more models to provide output data, asdiscussed above. Further, the output data is processed to provideindicator data, as discussed above. One or more graphicalrepresentations are provided (612). For example, the indicator data canbe processed to provide one or more condition indicators, e.g., gasmaps, within a graphical representation of the facility, e.g., asdepicted in FIGS. 5B and 5C, discussed above.

Implementations of the subject matter and the operations described inthis specification can be realized in digital electronic circuitry, orin computer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or inany appropriate combinations thereof. Implementations of the subjectmatter described in this specification can be realized using one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus, e.g., one or moreprocessors. In some examples, program instructions can be encoded on anartificially generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. In some examples, the dataprocessing apparatus can include special purpose logic circuitry, e.g.,an FPGA (field programmable gate array) or an ASIC (application specificintegrated circuit). In some examples, the data processing apparatus canalso include, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. Elements of a computer can include aprocessor for performing actions in accordance with instructions and oneor more memory devices for storing instructions and data. Generally, acomputer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto optical disks; and CD ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, e.g., as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such back end, middleware, or front endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. Examples of communication networks include a mesh network, alocal area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyimplementation of the present disclosure or of what may be claimed, butrather as descriptions of features specific to example implementations.Certain features that are described in this specification in the contextof separate implementations can also be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation can also be implemented inmultiple implementations separately or in any suitable sub-combination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

What is claimed is:
 1. A computer-implemented method for predictinghazardous conditions at a facility, the method being executed using oneor more processors and comprising: receiving data associated with asite, the site being susceptible to potentially hazardous conditions;processing, by the one or more processors, the data, one or more models,and one or more prediction rules; determining, by the one or moreprocessors, that a hazardous condition is predicted to occur at thesite; providing, by the one or more processors, output data reflectingthe hazardous condition; processing, by the one or more processors, theoutput data to provide indicator data for providing a graphicalrepresentation of the site, the graphical representation providing agraphical depiction of the hazardous condition; and providing theindicator data to one or more user devices, the indicator data beingprocessed by each of the one or more user devices to display thegraphical representation.